A linear operator on a Banach space is called a dissipative operator when
On a Hilbert space, using the inner product linear in its first argument,
If , this proves the defining inequality. Conversely that inequality implies for every ; letting gives
For the next spectral assertion, dissipativity must be retained as a hypothesis. Let be closed, densely defined and dissipative. For , the dissipative bound makes injective. Its range is closed: if , the bound makes Cauchy, and then converges. Closedness produces a preimage of . If the range is all of , the same bound gives a bounded inverse, so is in the resolvent set. If the closed range is proper, there is a nonzero vector in its orthogonal complement, and the definition of the adjoint of a densely defined operator gives
This proves that the positive spectrum of a dissipative operator is adjoint point spectrum:
Without dissipativity the PDF's assertion is false. For example, multiplication by on is bounded, closed and densely defined. The value is spectral: normalized functions supported closer and closer to make tend to zero. But has no eigenvector for eigenvalue , since a square-integrable function supported on the singleton is zero.
For the given maximal-domain operator , finite-support sequences belong to , so the domain is dense. Coordinatewise limits also show it is closed: if and in , each defining coordinate equation passes to the limit, giving and hence .
Put and . For ,
Since by Cauchy-Schwarz inequality, has a finite limit . Its increments are square summable as well. Direct summation gives
The original inner-product series converges because . Taking therefore gives
Thus is dissipative, including its full maximal domain; the limiting boundary term has not been discarded.
To decide generation, solve for arbitrary and fixed . The first coordinate gives , and the rest give
Because all denominators are at least one,
Hence is bounded, so belongs to . The coordinate equations give , proving and surjectivity of . Together with density, closedness and dissipativity, the Lumer-Phillips theorem proves the contraction generator from an exponentially weighted difference operator conclusion:
The domain is crucial. The formal transposed matrix suggests a positive-eigenvalue solution
which is square summable because the products converge. It is not a true adjoint eigenvector: the admissible test vector has , so , while . These cannot agree for unless . This exemplifies how boundary terms exclude formal adjoint eigenvectors, and explains why a calculation using only finite-support test vectors would give the wrong generation answer.